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The release 1.2.0 of the Bob signal-processing and machine learning toolbox is available (www.idiap.ch/software/bob)

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The release 1.2.0 of the Bob signal-processing and machine learning toolbox is available (www.idiap.ch/software/bob)

Bob provides both efficient implementations of several machine learning algorithms as well as a framework to help researchers to publish reproducible research.
It is developed by the Biometrics Group ( http://www.idiap.ch/~marcel/professional/Research_Team.html ) at Idiap in Switzerland.

The new release of Bob has brought the following features and/or improvements, such as:* Unified implementation of Local Binary Patterns (LBPs),* Histograms of Oriented Gradients (HOG) implementation,* Total variability (i-vector) implementation,* Conjugate gradient based-implementation for logistic regression,* Improved multi-layer perceptrons implementation (Back-propagation can now be easily used in combination with any optimizer -- i.e L-BFGS),* Pseudo-inverse-based method for Linear Discriminant Analysis,* Covariance-based method for Principal Component Analysis,* Whitening and within-class covariance normalization techniques,* Module for object detection and keypoint localization (bob.visioner),* Module for audio processing such as LFCC and MFCC ( http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/TutorialsAP.html ),* Improved extensions (satellite packages), that now support both Python and C++ code, within an easy to use framework,* Improved documentation and add new tutorials,* Support for Intel's MKL (in addition to ATLAS),* Extend supported platforms (Arch Linux).